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Prognostic efficacy of the RTN1 gene in patients with diffuse large B-cell lymphoma.

Scientific reports | 2021

Gene expression profiling has been vastly used to extract the genes that can predict the clinical outcome in patients with diverse cancers, including diffuse large B-cell lymphoma (DLBCL). With the aid of bioinformatics and computational analysis on gene expression data, various prognostic gene signatures for DLBCL have been recently developed. The major drawback of the previous signatures is their inability to correctly predict survival in external data sets. In other words, they are not reproducible in other datasets. Hence, in this study, we sought to determine the gene(s) that can reproducibly and robustly predict survival in patients with DLBCL. Gene expression data were extracted from 7 datasets containing 1636 patients (GSE10846 [n = 420], GSE31312 [n = 470], GSE11318 [n = 203], GSE32918 [n = 172], GSE4475 [n = 123], GSE69051 [n = 157], and GSE34171 [n = 91]). Genes significantly associated with overall survival were detected using the univariate Cox proportional hazards analysis with a P value < 0.001 and a false discovery rate (FDR) < 5%. Thereafter, significant genes common between all the datasets were extracted. Additionally, chromosomal aberrations in the corresponding region of the final common gene(s) were evaluated as copy number alterations using the single nucleotide polymorphism (SNP) data of 570 patients with DLBCL (GSE58718 [n = 242], GSE57277 [n = 148], and GSE34171 [n = 180]). Our results indicated that reticulon family gene 1 (RTN1) was the only gene that met our rigorous pipeline criteria and associated with a favorable clinical outcome in all the datasets (P < 0.001, FDR < 5%). In the multivariate Cox proportional hazards analysis, this gene remained independent of the routine international prognostic index components (i.e., age, stage, lactate dehydrogenase level, Eastern Cooperative Oncology Group [ECOG] performance status, and number of extranodal sites) (P < 0.0001). Furthermore, no significant chromosomal aberration was found in the RTN1 genomic region (14q23.1: Start 59,595,976/End 59,870,966).

Pubmed ID: 34702929 RIS Download

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This is a list of tools and resources that we have found mentioned in this publication.


PennCNV (tool)

RRID:SCR_002518

A free software tool for Copy Number Variation (CNV) detection from SNP genotyping arrays. Currently it can handle signal intensity data from Illumina and Affymetrix arrays. With appropriate preparation of file format, it can also handle other types of SNP arrays and oligonucleotide arrays. PennCNV implements a hidden Markov model (HMM) that integrates multiple sources of information to infer CNV calls for individual genotyped samples. It differs form segmentation-based algorithm in that it considered SNP allelic ratio distribution as well as other factors, in addition to signal intensity alone. In addition, PennCNV can optionally utilize family information to generate family-based CNV calls by several different algorithms. Furthermore, PennCNV can generate CNV calls given a specific set of candidate CNV regions, through a validation-calling algorithm.

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NCBI Epigenomics (tool)

RRID:SCR_006151

THIS RESOURCE IS NO LONGER IN SERVICE, documented on January 19, 2022.

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